import pickle
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
%matplotlib qt
def Calib2Undistort(input_dir, src_file, output_dir, corners_x, corners_y):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((corners_x*corners_y,3), np.float32)
objp[:,:2] = np.mgrid[0:corners_x, 0:corners_y].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob(input_dir +'/'+ src_file)
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (corners_x,corners_y), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (corners_x,corners_y), corners, ret)
_, _write_name = fname.split("\\")
write_name = output_dir+'/dc_'+_write_name
cv2.imwrite(write_name, img)
#calibrate camera
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
#undistort the image
dst = cv2.undistort(img, mtx, dist, None, mtx)
write_name = output_dir +'/ud_'+_write_name
cv2.imwrite(write_name, dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )
Calib2Undistort("camera_cal","calibration*.jpg","output_images",9,6)
# Read in the saved objpoints and imgpoints
dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
images = glob.glob("test_images/*.jpg")
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = plt.imread(fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
_, _write_name = fname.split("\\")
write_name = "undistorted/"+_write_name
plt.imsave(write_name, img)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
images = glob.glob("undistorted/*.jpg")
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = plt.imread(fname)
# Convert to HLS color space and separate the S channel
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
# Convert to HSV color space and separate the V channel
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
v_channel = hsv[:,:,2]
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Sobel x
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold color channel S
thresh_min = 50
thresh_max = 255
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Threshold color channel V
v_thresh_min = 200
v_thresh_max = 255
v_binary = np.zeros_like(v_channel)
v_binary[(v_channel >= v_thresh_min) & (v_channel <= v_thresh_max)] = 1
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, v_binary)) * 255
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[(v_binary == 1) | (sxbinary == 1)] = 1
_, _write_name = fname.split("\\")
write_name = "s_gradient/"+_write_name
plt.imsave(write_name, combined_binary, cmap='gray')
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Stacked thresholds')
ax1.imshow(color_binary)
ax2.set_title('Combined S channel and V channel thresholds')
ax2.imshow(combined_binary, cmap='gray')
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
`vertices` should be a numpy array of integer points.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
sROI = [[210,720],[1100,720],[720,470],[565,470]]
dROI = [[320,720],[960,720],[960,0],[320,0]]
# Define a function that takes an image, number of x and y points,
# camera matrix and distortion coefficients
def corners_unwarp(img, sROI, dROI):
# Grab the image shape
img_size = (img.shape[1], img.shape[0])
# For source points I'm grabbing the outer four detected corners
src = np.float32([sROI[0], sROI[1], sROI[2], sROI[3]])
# For destination points, I'm arbitrarily choosing some points to be
# a nice fit for displaying our warped result
# again, not exact, but close enough for our purposes
dst = np.float32([dROI[0], dROI[1], dROI[2], dROI[3]])
# Given src and dst points, calculate the perspective transform matrix
M = cv2.getPerspectiveTransform(src, dst)
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(img, M, img_size,flags=cv2.INTER_LINEAR)
# Return the resulting image and matrix
return warped, M
# Read in the saved objpoints and imgpoints
dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
images = glob.glob("s_gradient/*.jpg")
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = plt.imread(fname)
#cv2.fillPoly(img, np.array([[(210,720),(1100,720),(720,470),(565,470)]]), 255)
img_masked = region_of_interest(img, np.array([[(210-20, 720), (1100+20, 720),(720+10, 470-10),(565-10,470-10)]]))
warped, M = corners_unwarp(img_masked, sROI, dROI)
_, _write_name = fname.split("\\")
write_name = "b_warped/"+_write_name
plt.imsave(write_name, warped, cmap='gray')
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Combined S channel and gradient thresholds')
ax1.imshow(img, cmap='gray')
ax2.set_title('Perspective Transformed')
ax2.imshow(warped, cmap='gray')
def find_lane_pixels(binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
### TO-DO: Find the four below boundaries of the window ###
win_xleft_low = leftx_current - margin # Update this
win_xleft_high = leftx_current + margin # Update this
win_xright_low = rightx_current - margin # Update this
win_xright_high = rightx_current + margin # Update this
# Draw the windows on the visualization image
#cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
#cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
### TO-DO: Identify the nonzero pixels in x and y within the window ###
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
### TO-DO: If you found > minpix pixels, recenter next window ###
### (`right` or `leftx_current`) on their mean position ###
#pass # Remove this when you add your function
print((good_left_inds))
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped[:,:,0])
### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
## Visualization ##
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
#plt.plot(left_fitx, ploty, color='yellow')
#plt.plot(right_fitx, ploty, color='yellow')
return out_img, left_fit, right_fit, left_fitx, right_fitx
left_fit = np.array([ 2.13935315e-04, -3.77507980e-01, 4.76902175e+02])
right_fit = np.array([4.17622148e-04, -4.93848953e-01, 1.11806170e+03])
def fit_poly(img_shape, leftx, lefty, rightx, righty):
global left_fit, right_fit
### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty
def search_around_poly(binary_warped):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Plot the polynomial lines onto the image
#plt.plot(left_fitx, ploty, color='yellow')
#plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
return result, left_fit, right_fit, left_fitx, right_fitx
images = glob.glob("b_warped/*.jpg")
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = plt.imread(fname)
img_fitted, left_fit, right_fit, left_fitx, right_fitx = search_around_poly(img[:,:,0])
_, _write_name = fname.split("\\")
write_name = "polyfit/"+_write_name
plt.imsave(write_name, img_fitted, cmap='gray')
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Combined S channel and gradient thresholds')
ax1.imshow(img, cmap='gray')
ax2.set_title('PolyFit')
ax2.imshow(img_fitted, cmap='gray')
ploty = np.linspace(0, img.shape[0]-1, img.shape[0] )
ax2.plot(left_fitx, ploty, color='yellow')
ax2.plot(right_fitx, ploty, color='yellow')
def measure_curvature_real(img, left_fit, right_fit):
'''
Calculates the curvature of polynomial functions in meters.
'''
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Start by generating our fake example data
# Make sure to feed in your real data instead in your project!
#ploty, left_fit_cr, right_fit_cr = generate_data(ym_per_pix, xm_per_pix)
ploty = np.linspace(0, img.shape[0]-1, img.shape[0])
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
left_curverad = (0.5 / np.abs(left_fit[0]))*(1+(2*left_fit[0]*y_eval*ym_per_pix+left_fit[1])**2)**1.5
right_curverad = (0.5 / np.abs(right_fit[0]))*(1+(2*right_fit[0]*y_eval*ym_per_pix+right_fit[1])**2)**1.5
# Calculate offset
offset = ((left_fit[-1]+right_fit[-1])/2 - img.shape[1]/2) * xm_per_pix
return left_curverad, right_curverad, offset
images = glob.glob("b_warped/*.jpg")
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
warped = plt.imread(fname)[:,:,0]
img_fitted, left_fit, right_fit, left_fitx, right_fitx = search_around_poly(warped)
_, f = fname.split("\\")
undist = plt.imread("undistorted/"+f)
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Compute the inverse perspective transform:
src = np.float32([sROI[0], sROI[1], sROI[2], sROI[3]])
dst = np.float32([dROI[0], dROI[1], dROI[2], dROI[3]])
Minv = cv2.getPerspectiveTransform(dst, src)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (warped.shape[1], warped.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
left_curverad, right_curverad, offset = measure_curvature_real(img_fitted, left_fit, right_fit)
txt1 = "Vehicle Offset to Middle Lane = " + str(round(offset, 2)) + " m"
if left_curverad < right_curverad:
txt2 = "Radius of Lane = " + str(int(left_curverad)) + " m"
else:
txt2 = "Radius of Lane = " + str(int(right_curverad)) + " m"
_, _write_name = fname.split("\\")
write_name = "warped/"+_write_name
plt.imsave(write_name, result)
# Plotting thresholded images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('Undistorted')
ax1.imshow(undist)
ax2.set_title('Warped')
ax2.imshow(result)
plt.text(50, 50, txt1)
plt.text(50, 100, txt2)